Was wondering how much has this evolved over the last 6 months, how viable machine learning algorithms are appliable and clusterable over NOSQL databases, and which ones can do this without dumping the entire database.
I had some links talking about it:
Full text search with MongoDB[2]
Ask HN: What's the best way to handle full-text search with MongoDB?[3]
AskHN: NoSQL with full text search - which is better CouchDB or MongoDB?[4]
Is MongoDB a valid alternative to relational db + lucene?[5]
[1] http://schedule.sxsw.com/events/event_IAP7455
[2] http://hmarr.com/2010/mar/18/full-text-search-with-mongodb/
[3] http://news.ycombinator.com/item?id=2069271
[4] http://news.ycombinator.com/item?id=1984666
[5] http://stackoverflow.com/questions/2546494/is-mongodb-a-vali...
[6] http://engineering.foursquare.com/2011/03/22/building-a-reco...
Riak also seems to have full-text search. Has anyone used it?
Riak isn't the fastest single node system, but if you're going big and need several servers anyway it will save you some time.
CouchDB could use elasticsearch and its streaming indexation ("river" -> http://www.elasticsearch.org/docs/elasticsearch/river/couchd... ) to get scalable fulltext search.
An interesting project for fulltext search when it comes to mongodb and SOLR is photovoltaic ( https://github.com/mikejs/photovoltaic ), it pipes the mongoDB changes to the Solr XML interface. Sadly, I haven't had time to use it yet, but it looks interesting.